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Very soon, many of your customers will have liaisons. And they won\u2019t be human.\u00a0We\u2019re entering the next frontier in commerce: the rise of Business-to-AI, or B2AI. AI agents are becoming a new customer segment. As AI agents increasingly mediate how people research, evaluate, and purchase products, companies will need to rethink how they show up\u00a0within those decision architectures.<\/p>\n
Recent\u00a0Visa research shows businesses are already preparing for this shift. Seventy-one percent of businesses say they are willing to optimize products and offers specifically for AI agents, and more than half say they would allow AI agents to negotiate prices or terms directly with other AI systems. And there are early signs that AI search traffic converts at remarkably high levels, jumpstarting\u00a0traditional acquisition funnels.<\/p>\n
This leap to B2AI will rewrite much of what we know about customer acquisition. You won\u2019t just be influencing people anymore. Increasingly, you will need to convince the AI systems that inform their decisions.<\/p>\n
For all intents and purposes, you should\u00a0treat\u00a0AI agents as thought-partners to the customer. And the core tenets of how to approach any thought-partner still apply, as long as you transcreate them to agentic structure: Meet the partner where they are; educate them about your point of view; mesh with their movements; build trust then convenience; ensure you\u2019re clear about your purpose.<\/p>\n
1.\u00a0Structure your data so machines can find \u2014 and trust \u2014 you.<\/p>\n
AI agents rely on structured signals, not just marketing copy. Product specifications, pricing, availability, and attributes should be organized so machines can easily evaluate and compare options.<\/p>\n
That means investing in structured product catalogs, consistent metadata, and standardized schemas that allow AI systems to interpret your offering accurately. If AI agents cannot clearly understand your product, they are unlikely to recommend it.<\/p>\n
Consider this a form of machine-readable shelf presence: just as a consumer-packaged goods brand invests in packaging legibility and planogram placement in a physical store, your brand now needs equivalent legibility inside the data environments AI agents browse. The format has changed; the principle hasn\u2019t.<\/p>\n
2.\u00a0Become a knowledge source, not just a product.<\/p>\n
AI\u00a0models\u00a0don\u2019t peruse, they ingest. As models evolve from retrieval tools into reasoning engines, the question is no longer whether an AI can find your product page. It\u2019s whether it can think with your brand\u2019s data. Product knowledge, FAQs, documentation, and brand facts should be structured so AI systems can easily\u00a0parse, interpret, and reference them \u2014 and not just as lookup sources, but as reference material the agent can reason from when building a recommendation.<\/p>\n
Think of it as building a library. A consumer reviewing your website needs to be persuaded. An AI agent ingesting your knowledge base needs to be informed. These are different design problems, and they require different investments, including structured knowledge graphs, well-tagged documentation, and brand facts organized for machine interpretation rather than human scanning.<\/p>\n
3.\u00a0Build for machine execution, not human navigation.<\/p>\n
Infrastructure certainty is non-negotiable:\u00a0agents don\u2019t automatically disqualify you for missing features \u2014 they disqualify you for missing data.<\/p>\n
AI agents are acutely sensitive to operational uncertainty in ways humans aren\u2019t. Inconsistent inventory signals, ambiguous pricing, or missing delivery windows don\u2019t frustrate an agent \u2014 they simply cause it to default to a competitor whose data it can execute against cleanly. Agents don\u2019t tolerate ambiguity.<\/p>\n
That means companies need to expose live pricing and availability through structured interfaces like\u00a0model context protocols (MCPs)\u00a0so AI systems can retrieve accurate, real-time data and complete transactions reliably.\u00a0This\u00a0isn\u2019t a feature launch \u2014 it\u2019s an OS rebuild for machine-to-machine interaction.<\/p>\n
4. Trust is as important as convenience.<\/p>\n
In an AI\u00a0world\u00a0growing\u00a0increasingly scarce of trust,\u00a0established\u00a0companies and\u00a0editorial brands\u00a0become even more important. You must build verifiable trust signals into your platform.<\/p>\n
As AI agents evaluate options, they will rely on signals that indicate credibility and reliability. Consistent brand data, transparent policies, secure payment infrastructure, and authoritative sources all influence whether an AI system recommends your product or moves on.<\/p>\n
Trust will increasingly function as a ranking signal in AI-mediated commerce. Brands that have built genuine trust signals \u2014 third-party reviews, consistent data across platforms, authoritative sourcing \u2014 will be harder to displace than offerings that have merely optimized for visibility. Ultimately, trust becomes the infrastructure.<\/p>\n
5. Purpose matters.<\/p>\n
Brand purpose has always mattered to consumers. Now it matters to their AI agents, too.<\/p>\n
AI systems don\u2019t just retrieve information \u2014 they evaluate it. And the signals they weigh go beyond structured data and pricing. Increasingly, AI reasoning engines assess quality, coherence, and authenticity when deciding which brands to recommend. A brand with a clearly articulated purpose woven through its content, policies, customer interactions, and sourcing practices gives an AI agent richer, more coherent material to reason from. A brand without that architecture looks thin by comparison: technically present, but difficult for an agent to build a confident case around.<\/p>\n
Think of it this way: when an AI agent evaluates two competing products \u2014 one from a brand with deep, consistent storytelling about why it exists and who it serves, and another from a brand optimized purely for volume and discoverability \u2014 the purpose-driven brand gives the agent more to work with. Its claims are substantiated across touchpoints. Its content has texture and specificity. Its reviews reflect a relationship with customers, not just transactions. The agent isn\u2019t making a moral judgment; it\u2019s making a quality assessment. And purpose, expressed as architectural consistency, reads as quality.<\/p>\n
This means the work of brand purpose isn\u2019t separate from B2AI strategy \u2014 it is\u00a0B2AI strategy. Because even in a machine-mediated marketplace, meaning still matters. And only humans can find the deeper meanings that connect us.<\/p>\n
English majors, rejoice.<\/p>\n
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.<\/p>\n
#Visa #CMO #agents #customers #heres #sell<\/p>\n","protected":false},"excerpt":{"rendered":"
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